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Localization of Sparse Transmural Excitation Stimuli from Surface Mapping

  • Jingjia Xu
  • Azar Rahimi Dehaghani
  • Fei Gao
  • Linwei Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7510)

Abstract

As in-silico 3D electrophysiological (EP) models start to play an essential role in revealing transmural EP characteristics and diseased substrates in individual hearts, there arises a critical challenge to properly initialize these models, i.e., determine the location of excitation stimuli without a trial-and-error process. In this paper, we present a novel method to localize transmural stimuli based on their spatial sparsity using surface mapping data. In order to overcome the mathematical ill-posedness caused by the limited measurement data, a neighborhood-smoothness constraint is used to first obtain a low-resolution estimation of sparse solution. This is then used to initialize an iterative, re-weighted minimum-norm regularization to enforce a sparse solution and thereby overcome the physical ill-posedness of the electromagnetic inverse problem. Phantom experiments are performed on a human heart-torso model to evaluate this method in localizing excitation stimuli at different regions and depths within the ventricles, as well as to test its feasibility in differentiating multiple remotely or close distributed stimuli. Real-data experiments are performed on a healthy and an infarcted porcine heart, where activation isochronous simulated with the reconstructed stimuli are significantly closer to the catheterized mapping data than other stimuli configurations. This method has the potential to benefit the current research in subject-specific EP modeling as well as to facilitate clinical decisions involving device pacing and ectopic foci.

Keywords

transmural electrophysiology in silico electrophysiological models sparse excitation stimuli surface mapping 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jingjia Xu
    • 1
  • Azar Rahimi Dehaghani
    • 1
  • Fei Gao
    • 1
  • Linwei Wang
    • 1
  1. 1.Computational Biomedicine Laboratory, Golisano College of Computing and Information SciencesRochester Institute of TechnologyRochesterUSA

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